Chen Li, Yu Guoqiang, Miller David J, Song Lei, Langefeld Carl, Herrington David, Liu Yongmei, Wang Yue
Dearptment of Electrical & Computer Engineering, Virginia Polytechnic Institute and State University.
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2009 Nov 1;1-4(Nov 2009):26-31. doi: 10.1109/BIBMW.2009.5332132.
Genome-wide association studies (GWAS) have been widely applied to identify informative SNPs associated with common and complex diseases. Besides single-SNP analysis, the interaction between SNPs is believed to play an important role in disease risk due to the complex networking of genetic regulations. While many approaches have been proposed for detecting SNP interactions, the relative performance and merits of these methods in practice are largely unclear. In this paper, a ground-truth based comparative study is reported involving 9 popular SNP detection methods using realistic simulation datasets. The results provide general characteristics and guidelines on these methods that may be informative to the biological investigators.
全基因组关联研究（GWAS）已被广泛应用于识别与常见复杂疾病相关的信息性单核苷酸多态性（SNP）。除了单SNP分析外，由于遗传调控的复杂网络，SNP之间的相互作用被认为在疾病风险中起着重要作用。虽然已经提出了许多方法来检测SNP相互作用，但这些方法在实际中的相对性能和优点在很大程度上尚不清楚。本文报道了一项基于真实模拟数据集的、涉及9种常用SNP检测方法的基于真实情况的比较研究。结果提供了这些方法的一般特征和指导原则，可能对生物学研究者有参考价值。
